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We are seeking an Senior Automation Engineer with 6+ years strong hands-on experience in AI/ML model validation and testing. This role focuses on ensuring the quality, reliability, accuracy, and robustness of AI/ML and LLM-based systems through systematic validation, data quality checks, and automated evaluation pipelines.
The ideal candidate has solid experience working with supervised ML models, LLMs, and ML pipelines, strong Python-based validation scripting skills, and a deep understanding of data, model behavior, and evaluation metrics.
Key Responsibilities
AI / ML Model Validation
- Design and execute validation strategies for AI/ML models, including supervised learning models and large language models (LLMs).
- Perform functional, regression, and behavioral testing of ML models across versions.
- Validate model outputs for accuracy, consistency, bias, and edge cases.
- Evaluate LLM responses for correctness, relevance, hallucinations, and safety.
Data Quality, Drift & Monitoring
- Design and implement data quality validation checks for training, validation, and inference datasets.
- Detect and analyze data drift and concept drift across model iterations and production data.
- Validate input data assumptions, feature distributions, and schema consistency.
- Collaborate with data and ML engineers to identify and resolve data-related issues impacting model performance.
Python-Based Validation & Automation
- Develop Python-based validation scripts and frameworks to automate:
- Model evaluation
- Data validation
- Regression testing across model versions
- Implement automated checks for model performance metrics (precision, recall, F1, accuracy, etc.).
- Build reusable validation utilities that integrate with ML workflows and pipelines.
LLM & Advanced AI Testing
- Execute LLM evaluation workflows, including:
- Prompt‑response validation
- Golden dataset comparisons
- Regression testing across prompt or model changes
- Contribute to evaluation strategies for RAG (Retrieval-Augmented Generation) or multi‑step LLM pipelines.
- Support responsible AI initiatives by testing bias, robustness, and failure modes.
Collaboration & Quality Ownership
- Work closely with ML engineers, data scientists, platforms, and product teams.
- Actively participate in requirement discussions and model review sessions.
- Clearly document test results, risks, and quality insights for stakeholders.
- Contribute to continuous improvement of AI testing practices and standards.
Required Skills & Qualifications
- 4+ years of hands-on experience in AI/ML model testing, validation, or quality engineering.
- Strong proficiency in Python for model validation and data analysis.
- Experience validating supervised ML models and/or LLM-based systems.
- Solid understanding of ML evaluation metrics and validation techniques.
- Experience with data quality checks, drift detection, and dataset validation.
- Familiarity with ML pipelines and model lifecycle (training, validation, inference).
- Ability to analyze model behavior and explain quality risks clearly.
- Good communication and collaboration skills.
Equal Employment Opportunity
Baxter is an equal opportunity employer. Baxter evaluates qualified applicants without regard to race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, protected veteran status, disability/handicap status or any other legally protected characteristic.
Reasonable Accommodations
Baxter is committed to working with and providing reasonable accommodations to individuals with disabilities globally. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application or interview process, please click on the link here and let us know the nature of your request along with your contact information.
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